

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case				:      Optional[Any]   		=			logging.get_logger(__name__)

__snake_case				:      int   		=			{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			'''mobilenet_v1'''





						def __init__(    self  ,      _lowercase=3  ,      _lowercase=224  ,      _lowercase=1.0  ,      _lowercase=8  ,      _lowercase="relu6"  ,      _lowercase=True  ,      _lowercase=0.9_9_9  ,      _lowercase=0.0_2  ,      _lowercase=0.0_0_1  ,      **_lowercase  ,      )     -> Tuple:
									super().__init__(**_lowercase    )

									if depth_multiplier <= 0:
												raise ValueError("""depth_multiplier must be greater than zero."""    )

									a_						:						Union[str, Any] 							= num_channels
									a_						:						Any 							= image_size
									a_						:						Optional[int] 							= depth_multiplier
									a_						:						Optional[Any] 							= min_depth
									a_						:						Tuple 							= hidden_act
									a_						:						Optional[int] 							= tf_padding
									a_						:						Tuple 							= classifier_dropout_prob
									a_						:						str 							= initializer_range
									a_						:						Optional[int] 							= layer_norm_eps







class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			version.parse('''1.11'''							)





						@property
						def 			UpperCamelCase__					(    self    )     -> Mapping[str, Mapping[int, str]]:
									return OrderedDict([("""pixel_values""", {0: """batch"""})]    )





						@property
						def 			UpperCamelCase__					(    self    )     -> Mapping[str, Mapping[int, str]]:
									if self.task == "image-classification":
												return OrderedDict([("""logits""", {0: """batch"""})]    )
									else:
												return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]    )





						@property
						def 			UpperCamelCase__					(    self    )     -> float:
									return 1e-4





from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


